code: def test_group_by_length(self):
      # Get some inputs of random lengths
      lengths = torch.randint(0, 25, (100,)).tolist()
      # Put one bigger than the others to check it ends up in first position
      lengths[32] = 50
  
      indices = list(LengthGroupedSampler(4, lengths=lengths))
      # The biggest element should be first
      self.assertEqual(lengths[indices[0]], 50)
      # The indices should be a permutation of range(100)
      self.assertEqual(sorted(indices), list(range(100)))
  
imports: import copy
  import unittest
  import numpy
  from transformers.data.data_collator import default_data_collator
  from transformers.testing_utils import require_accelerate, require_torch
  from transformers.trainer_utils import RemoveColumnsCollator, find_executable_batch_size
  from transformers.utils import is_torch_available
label_desc: Use image node_id 4 for calling the TrainerUtilsTest obj's underlying member method code with example usage: obj.test_group_by_length() without return types